Chapter

Advances in Neural Networks – ISNN 2007

Volume 4491 of the series Lecture Notes in Computer Science pp 403-412

A Hierarchical Learning System Incorporating with Supervised, Unsupervised and Reinforcement Learning

  • Jinglu HuAffiliated withCarnegie Mellon UniversityWaseda University, Kitakyushu, Fukuoka
  • , Takafumi SasakawaAffiliated withCarnegie Mellon UniversityWaseda University, Kitakyushu, Fukuoka
  • , Kotaro HirasawaAffiliated withCarnegie Mellon UniversityWaseda University, Kitakyushu, Fukuoka
  • , Huiru ZhengAffiliated withCarnegie Mellon UniversityUniversity of Ulster, N.Ireland

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Abstract

According to Hebb’s Cell assembly theory, the brain has the capability of function localization. On the other hand, it is suggested that the brain has three different learning paradigms: supervised, unsupervised and reinforcement learning. Inspired by the above knowledge of brain, we present a hierarchical learning system consisting of three parts: supervised learning (SL) part, unsupervised learning (UL) part and reinforcement learning (RL) part. The SL part is a main part learning input-output mapping; the UL part realizes the function localization of learning system by controlling firing strength of neurons in SL part based on input patterns; the RL part optimizes system performance by adjusting parameters in UL part. Simulation results confirm the effectiveness of the proposed hierarchical learning system.